Add VizDoom PPO example and results (#533)

* update vizdoom ppo example

* update README with results

examples/vizdoom/README.md

@@ -53,14 +53,35 @@ python3 replay.py maps/D4_battle2.cfg results/c51/d4.lmp
 
 See [maps/README.md](maps/README.md)
 
-## Algorithms
-
-The setting is exactly the same as Atari. You can definitely try more algorithms listed in Atari example.
-
 ## Reward
 
 1. living reward is bad
 2. combo-action is really important
 3. negative reward for health and ammo2 is really helpful for d3/d4
 4. only with positive reward for health is really helpful for d1
 5. remove MOVE_BACKWARD may converge faster but the final performance may be lower
+
+## Algorithms
+
+The setting is exactly the same as Atari. You can definitely try more algorithms listed in Atari example.
+
+### C51 (single run)
+
+| task                        | best reward | reward curve                          | parameters                                                   |
+| --------------------------- | ----------- | ------------------------------------- | ------------------------------------------------------------ |
+| D2_navigation          | 747.52          | ![](results/c51/D2_navigation_rew.png)         | `python3 vizdoom_c51.py --task "D2_navigation"` |
+| D3_battle              | 1855.29          | ![](results/c51/D3_battle_rew.png)         | `python3 vizdoom_c51.py --task "D3_battle"` |
+
+### PPO (single run)
+
+| task                        | best reward | reward curve                          | parameters                                                   |
+| --------------------------- | ----------- | ------------------------------------- | ------------------------------------------------------------ |
+| D2_navigation          | 770.75          | ![](results/ppo/D2_navigation_rew.png)         | `python3 vizdoom_ppo.py --task "D2_navigation"` |
+| D3_battle              | 320.59          | ![](results/ppo/D3_battle_rew.png)         | `python3 vizdoom_ppo.py --task "D3_battle"` |
+
+### PPO with ICM (single run)
+
+| task                        | best reward | reward curve                          | parameters                                                   |
+| --------------------------- | ----------- | ------------------------------------- | ------------------------------------------------------------ |
+| D2_navigation          | 844.99          | ![](results/ppo_icm/D2_navigation_rew.png)         | `python3 vizdoom_ppo.py --task "D2_navigation" --icm-lr-scale 10` |
+| D3_battle              | 547.08          | ![](results/ppo_icm/D3_battle_rew.png)         | `python3 vizdoom_ppo.py --task "D3_battle" --icm-lr-scale 10` |

examples/vizdoom/vizdoom_a2c_icm.py → examples/vizdoom/vizdoom_ppo.py

@@ -6,11 +6,12 @@
 import torch
 from env import Env
 from network import DQN
+from torch.optim.lr_scheduler import LambdaLR
 from torch.utils.tensorboard import SummaryWriter
 
 from tianshou.data import Collector, VectorReplayBuffer
 from tianshou.env import ShmemVectorEnv
-from tianshou.policy import A2CPolicy, ICMPolicy
+from tianshou.policy import ICMPolicy, PPOPolicy
 from tianshou.trainer import onpolicy_trainer
 from tianshou.utils import TensorboardLogger
 from tianshou.utils.net.common import ActorCritic
@@ -21,18 +22,28 @@ def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument('--task', type=str, default='D2_navigation')
     parser.add_argument('--seed', type=int, default=0)
-    parser.add_argument('--buffer-size', type=int, default=2000000)
-    parser.add_argument('--lr', type=float, default=0.0001)
+    parser.add_argument('--buffer-size', type=int, default=100000)
+    parser.add_argument('--lr', type=float, default=0.00002)
     parser.add_argument('--gamma', type=float, default=0.99)
     parser.add_argument('--epoch', type=int, default=300)
     parser.add_argument('--step-per-epoch', type=int, default=100000)
-    parser.add_argument('--episode-per-collect', type=int, default=10)
-    parser.add_argument('--update-per-step', type=float, default=0.1)
-    parser.add_argument('--update-per-step', type=int, default=1)
-    parser.add_argument('--batch-size', type=int, default=64)
-    parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[512])
+    parser.add_argument('--step-per-collect', type=int, default=1000)
+    parser.add_argument('--repeat-per-collect', type=int, default=4)
+    parser.add_argument('--batch-size', type=int, default=256)
+    parser.add_argument('--hidden-size', type=int, default=512)
     parser.add_argument('--training-num', type=int, default=10)
     parser.add_argument('--test-num', type=int, default=100)
+    parser.add_argument('--rew-norm', type=int, default=False)
+    parser.add_argument('--vf-coef', type=float, default=0.5)
+    parser.add_argument('--ent-coef', type=float, default=0.01)
+    parser.add_argument('--gae-lambda', type=float, default=0.95)
+    parser.add_argument('--lr-decay', type=int, default=True)
+    parser.add_argument('--max-grad-norm', type=float, default=0.5)
+    parser.add_argument('--eps-clip', type=float, default=0.2)
+    parser.add_argument('--dual-clip', type=float, default=None)
+    parser.add_argument('--value-clip', type=int, default=0)
+    parser.add_argument('--norm-adv', type=int, default=1)
+    parser.add_argument('--recompute-adv', type=int, default=0)
     parser.add_argument('--logdir', type=str, default='log')
     parser.add_argument('--render', type=float, default=0.)
     parser.add_argument(
@@ -75,7 +86,7 @@ def get_args():
     return parser.parse_args()
 
 
-def test_a2c(args=get_args()):
+def test_ppo(args=get_args()):
     args.cfg_path = f"maps/{args.task}.cfg"
     args.wad_path = f"maps/{args.task}.wad"
     args.res = (args.skip_num, 84, 84)
@@ -105,33 +116,65 @@ def test_a2c(args=get_args()):
     test_envs.seed(args.seed)
     # define model
     net = DQN(
-        *args.state_shape, args.action_shape, device=args.device, features_only=True
+        *args.state_shape,
+        args.action_shape,
+        device=args.device,
+        features_only=True,
+        output_dim=args.hidden_size
     )
-    actor = Actor(
-        net, args.action_shape, hidden_sizes=args.hidden_sizes, device=args.device
-    )
-    critic = Critic(net, hidden_sizes=args.hidden_sizes, device=args.device)
+    actor = Actor(net, args.action_shape, device=args.device, softmax_output=False)
+    critic = Critic(net, device=args.device)
     optim = torch.optim.Adam(ActorCritic(actor, critic).parameters(), lr=args.lr)
+
+    lr_scheduler = None
+    if args.lr_decay:
+        # decay learning rate to 0 linearly
+        max_update_num = np.ceil(
+            args.step_per_epoch / args.step_per_collect
+        ) * args.epoch
+
+        lr_scheduler = LambdaLR(
+            optim, lr_lambda=lambda epoch: 1 - epoch / max_update_num
+        )
+
     # define policy
-    dist = torch.distributions.Categorical
-    policy = A2CPolicy(actor, critic, optim, dist).to(args.device)
+    def dist(p):
+        return torch.distributions.Categorical(logits=p)
+
+    policy = PPOPolicy(
+        actor,
+        critic,
+        optim,
+        dist,
+        discount_factor=args.gamma,
+        gae_lambda=args.gae_lambda,
+        max_grad_norm=args.max_grad_norm,
+        vf_coef=args.vf_coef,
+        ent_coef=args.ent_coef,
+        reward_normalization=args.rew_norm,
+        action_scaling=False,
+        lr_scheduler=lr_scheduler,
+        action_space=env.action_space,
+        eps_clip=args.eps_clip,
+        value_clip=args.value_clip,
+        dual_clip=args.dual_clip,
+        advantage_normalization=args.norm_adv,
+        recompute_advantage=args.recompute_adv
+    ).to(args.device)
     if args.icm_lr_scale > 0:
         feature_net = DQN(
             *args.state_shape,
             args.action_shape,
             device=args.device,
-            features_only=True
+            features_only=True,
+            output_dim=args.hidden_size
         )
         action_dim = np.prod(args.action_shape)
         feature_dim = feature_net.output_dim
         icm_net = IntrinsicCuriosityModule(
-            feature_net.net,
-            feature_dim,
-            action_dim,
-            hidden_sizes=args.hidden_sizes,
-            device=args.device
+            feature_net.net, feature_dim, action_dim, device=args.device
         )
-        icm_optim = torch.optim.adam(icm_net.parameters(), lr=args.lr)
+        icm_optim = torch.optim.Adam(icm_net.parameters(), lr=args.lr)
         policy = ICMPolicy(
             policy, icm_net, icm_optim, args.icm_lr_scale, args.icm_reward_scale,
             args.icm_forward_loss_weight
@@ -153,7 +196,8 @@ def test_a2c(args=get_args()):
     train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
     test_collector = Collector(policy, test_envs, exploration_noise=True)
     # log
-    log_path = os.path.join(args.logdir, args.task, 'a2c')
+    log_name = 'ppo_icm' if args.icm_lr_scale > 0 else 'ppo'
+    log_path = os.path.join(args.logdir, args.task, log_name)
     writer = SummaryWriter(log_path)
     writer.add_text("args", str(args))
     logger = TensorboardLogger(writer)
@@ -162,10 +206,15 @@ def save_fn(policy):
         torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
 
     def stop_fn(mean_rewards):
-        return False
+        if env.spec.reward_threshold:
+            return mean_rewards >= env.spec.reward_threshold
+        elif 'Pong' in args.task:
+            return mean_rewards >= 20
+        else:
+            return False
 
+    # watch agent's performance
     def watch():
-        # watch agent's performance
         print("Setup test envs ...")
         policy.eval()
         test_envs.seed(args.seed)
@@ -210,7 +259,7 @@ def watch():
         args.repeat_per_collect,
         args.test_num,
         args.batch_size,
-        episode_per_collect=args.episode_per_collect,
+        step_per_collect=args.step_per_collect,
         stop_fn=stop_fn,
         save_fn=save_fn,
         logger=logger,
@@ -222,4 +271,4 @@ def watch():
 
 
 if __name__ == '__main__':
-    test_a2c(get_args())
+    test_ppo(get_args())